The subject disclosure is directed to the imaging arts, digital photography arts, workflow arts, database arts, electronic storage arts, semantic network arts, and the like.
A personalized print artifact is generally a user-created document incorporating one or more photographs or images, such as a wedding album, photo-calendar, holiday card, party poster, thank you card, or the like. When creating such personalized print artifacts, the user must search through their personal collection(s) of photographs to locate the best match to the theme of their printed item, e.g., fall, Wedding, baseball, soccer, dance, school, Halloween, Christmas, Easter, Mother's Day, Father's Day, etc. However, most users have a limited set of personal photos with which to work, i.e., they are unlikely to have high-quality photographs of every single keyword (e.g., bat, ball, flowers, wedding, goal, ghost, witch, leaves, costumes, etc.) related to a particular theme.
Currently, a user utilizes the following workflow to search for a specific image (or set of images) to match a desired theme (e.g., October). First, the user provides the system with a photo-collection P, the desired theme T, and an initial set of search words, W0. The system then searches through the photo-collection for images R that “match” the initial search criteria (for example, by searching the images' metadata for the desired keywords), and then displays these results to the user. The user then evaluates the results, and determines if a suitable match has been found. If no match is found, then the user must select a new set of search words and start the process over again.
The above-described workflow uses pre-existing metadata to identify the content of the image; more specifically, it uses metadata that has been pre-generated using any number of methods, such as manual tagging, crowd sourcing using a photo-sharing website, face recognition algorithms, EXIF geo-location data, etc. A variation on this workflow uses advanced image processing techniques, such as Xerox's Generic Visual Categorizer in U.S. Pat. No. 7,756,341 (the entirety of which is incorporated by reference herein) to look for additional matches to the user-supplied keywords. Such advanced techniques can be used to analyze and index the user's photo-collection automatically. In this way, users do not need to manually tag every single photo in their photo-collection.
Despite this improvement, a common problem of both avenues is that their success relies heavily on the proper choice of the initial search words. For example, the simple search technique described above will be unsuccessful if none of the images contain the initial search words as part of the keywords in their meta-data. Similarly, the more advanced search technique will fail if none of the images are immediately recognizable as the object or person (or place, or thing) described by the initial search words.
Thus, it would be advantageous to provide an efficient system and method for identifying appropriate keywords related to a theme T and using such keywords to locate the most relevant images for the theme.